






import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors

@require_tokenizers
class 				lowerCAmelCase_	(		__snake_case   ,		unittest.TestCase			):
				_UpperCamelCase					:       List[Any]							       = MvpTokenizer
				_UpperCamelCase					:       Optional[Any]							       = MvpTokenizerFast
				_UpperCamelCase					:       List[Any]							       = True
				_UpperCamelCase					:       Dict							       = filter_roberta_detectors


				def 						__a					(					self      ):
											super().setUp()
											_lowercase							:   Optional[int] 							=   [
											    'l',
											    'o',
											    'w',
											    'e',
											    'r',
											    's',
											    't',
											    'i',
											    'd',
											    'n',
											    '\u0120',
											    '\u0120l',
											    '\u0120n',
											    '\u0120lo',
											    '\u0120low',
											    'er',
											    '\u0120lowest',
											    '\u0120newer',
											    '\u0120wider',
											    '<unk>',
											]
											_lowercase							:   str 							=   dict(zip(_lowerCAmelCase ,						range(len(_lowerCAmelCase      )      )      )      )
											_lowercase							:   Optional[int] 							=   ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
											_lowercase							:   Optional[Any] 							=   {'unk_token': '<unk>'}

											_lowercase							:   str 							=   os.path.join(self.tmpdirname ,						VOCAB_FILES_NAMES['vocab_file']      )
											_lowercase							:   str 							=   os.path.join(self.tmpdirname ,						VOCAB_FILES_NAMES['merges_file']      )
											with open(self.vocab_file ,						'w' ,						encoding='utf-8'      ) as fp:
																		fp.write(json.dumps(_lowerCAmelCase      ) + '\n'      )
											with open(self.merges_file ,						'w' ,						encoding='utf-8'      ) as fp:
																		fp.write('\n'.join(_lowerCAmelCase      )      )


				def 						__a					(					self ,						**_lowerCAmelCase      ):
											kwargs.update(self.special_tokens_map      )
											return self.tokenizer_class.from_pretrained(self.tmpdirname ,						**_lowerCAmelCase      )


				def 						__a					(					self ,						**_lowerCAmelCase      ):
											kwargs.update(self.special_tokens_map      )
											return self.rust_tokenizer_class.from_pretrained(self.tmpdirname ,						**_lowerCAmelCase      )


				def 						__a					(					self ,						_lowerCAmelCase      ):
											return "lower newer", "lower newer"


				@cached_property
				def 						__a					(					self      ):
											return MvpTokenizer.from_pretrained('RUCAIBox/mvp'      )


				@cached_property
				def 						__a					(					self      ):
											return MvpTokenizerFast.from_pretrained('RUCAIBox/mvp'      )


				@require_torch
				def 						__a					(					self      ):
											_lowercase							:   int 							=   ['A long paragraph for summarization.', 'Another paragraph for summarization.']
											_lowercase							:   Tuple 							=   [0, 2_5_0, 2_5_1, 1_7_8_1_8, 1_3, 3_9_1_8_6, 1_9_3_8, 4, 2]

											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		_lowercase							:   List[Any] 							=   tokenizer(_lowerCAmelCase ,						max_length=len(_lowerCAmelCase      ) ,						padding=_lowerCAmelCase ,						return_tensors='pt'      )
																		self.assertIsInstance(_lowerCAmelCase ,						_lowerCAmelCase      )

																		self.assertEqual((2, 9) ,						batch.input_ids.shape      )
																		self.assertEqual((2, 9) ,						batch.attention_mask.shape      )
																		_lowercase							:   str 							=   batch.input_ids.tolist()[0]
																		self.assertListEqual(_lowerCAmelCase ,						_lowerCAmelCase      )
																		# Test that special tokens are reset


				@require_torch
				def 						__a					(					self      ):
											_lowercase							:   Dict 							=   ['A long paragraph for summarization.', 'Another paragraph for summarization.']
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		_lowercase							:   str 							=   tokenizer(_lowerCAmelCase ,						padding=_lowerCAmelCase ,						return_tensors='pt'      )
																		# check if input_ids are returned and no labels
																		self.assertIn('input_ids' ,						_lowerCAmelCase      )
																		self.assertIn('attention_mask' ,						_lowerCAmelCase      )
																		self.assertNotIn('labels' ,						_lowerCAmelCase      )
																		self.assertNotIn('decoder_attention_mask' ,						_lowerCAmelCase      )


				@require_torch
				def 						__a					(					self      ):
											_lowercase							:   Optional[int] 							=   [
											    'Summary of the text.',
											    'Another summary.',
											]
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		_lowercase							:   List[str] 							=   tokenizer(text_target=_lowerCAmelCase ,						max_length=3_2 ,						padding='max_length' ,						return_tensors='pt'      )
																		self.assertEqual(3_2 ,						targets['input_ids'].shape[1]      )


				@require_torch
				def 						__a					(					self      ):
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		_lowercase							:   Optional[int] 							=   tokenizer(
																		    ['I am a small frog' * 1_0_2_4, 'I am a small frog'] ,						padding=_lowerCAmelCase ,						truncation=_lowerCAmelCase ,						return_tensors='pt'      )
																		self.assertIsInstance(_lowerCAmelCase ,						_lowerCAmelCase      )
																		self.assertEqual(batch.input_ids.shape ,						(2, 1_0_2_4)      )


				@require_torch
				def 						__a					(					self      ):
											_lowercase							:   Tuple 							=   ['A long paragraph for summarization.']
											_lowercase							:   Any 							=   [
											    'Summary of the text.',
											]
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		_lowercase							:   Optional[Any] 							=   tokenizer(_lowerCAmelCase ,						text_target=_lowerCAmelCase ,						return_tensors='pt'      )
																		_lowercase							:   Any 							=   inputs['input_ids']
																		_lowercase							:   Union[str, Any] 							=   inputs['labels']
																		self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()      )
																		self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()      )
																		self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()      )
																		self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()      )


				def 						__a					(					self      ):
											pass


				def 						__a					(					self      ):
											for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																		with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""      ):
																									_lowercase							:   Optional[int] 							=   self.rust_tokenizer_class.from_pretrained(_lowerCAmelCase ,						**_lowerCAmelCase      )
																									_lowercase							:   Optional[Any] 							=   self.tokenizer_class.from_pretrained(_lowerCAmelCase ,						**_lowerCAmelCase      )
																									_lowercase							:   Optional[Any] 							=   'A, <mask> AllenNLP sentence.'
																									_lowercase							:   Union[str, Any] 							=   tokenizer_r.encode_plus(_lowerCAmelCase ,						add_special_tokens=_lowerCAmelCase ,						return_token_type_ids=_lowerCAmelCase      )
																									_lowercase							:   Optional[Any] 							=   tokenizer_p.encode_plus(_lowerCAmelCase ,						add_special_tokens=_lowerCAmelCase ,						return_token_type_ids=_lowerCAmelCase      )

																									# token_type_ids should put 0 everywhere
																									self.assertEqual(sum(tokens_r['token_type_ids']      ) ,						sum(tokens_p['token_type_ids']      )      )

																									# attention_mask should put 1 everywhere, so sum over length should be 1
																									self.assertEqual(
																									    sum(tokens_r['attention_mask']      ) / len(tokens_r['attention_mask']      ) ,						sum(tokens_p['attention_mask']      ) / len(tokens_p['attention_mask']      ) ,						)

																									_lowercase							:   Dict 							=   tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']      )
																									_lowercase							:   Dict 							=   tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids']      )

																									# Rust correctly handles the space before the mask while python doesnt
																									self.assertSequenceEqual(tokens_p['input_ids'] ,						[0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]      )
																									self.assertSequenceEqual(tokens_r['input_ids'] ,						[0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]      )

																									self.assertSequenceEqual(
																									    _lowerCAmelCase ,						['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']      )
																									self.assertSequenceEqual(
																									    _lowerCAmelCase ,						['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']      )







import math

import tensorflow as tf
from packaging import version






def        snake_case__		(    lowerCamelCase_		):
							A      :					Optional[int]      		=					tf.convert_to_tensor(lowerCamelCase_		)
							A      :					Union[str, Any]      		=					0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0		)     ,				x.dtype		)		))

							return x * cdf






def        snake_case__		(    lowerCamelCase_		):
							A      :					List[str]      		=					tf.convert_to_tensor(lowerCamelCase_		)
							A      :					Any      		=					tf.cast(math.pi     ,				x.dtype		)
							A      :					int      		=					tf.cast(0.044_715     ,				x.dtype		)
							A      :					Tuple      		=					0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi		) * (x + coeff * tf.pow(lowerCamelCase_     ,				3		))		))

							return x * cdf






def        snake_case__		(    lowerCamelCase_		):
							A      :					Union[str, Any]      		=					tf.convert_to_tensor(lowerCamelCase_		)

							return x * tf.tanh(tf.math.softplus(lowerCamelCase_		)		)






def        snake_case__		(    lowerCamelCase_		):
							A      :					Optional[int]      		=					tf.convert_to_tensor(lowerCamelCase_		)
							A      :					List[Any]      		=					tf.cast(0.044_715     ,				x.dtype		)
							A      :					str      		=					tf.cast(0.7_978_845_608     ,				x.dtype		)

							return 0.5 * x * (1.0 + tf.tanh(x * coeffa * (1.0 + coeffa * x * x)		))






def        snake_case__		(    lowerCamelCase_		):
							A      :					List[str]      		=					tf.convert_to_tensor(lowerCamelCase_		)
							A      :					Any      		=					tf.cast(1.702     ,				x.dtype		)
							return x * tf.math.sigmoid(coeff * x		)






def        snake_case__		(    lowerCamelCase_		):
							return tf.clip_by_value(_gelu(lowerCamelCase_		)     ,				-10     ,				10		)






def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_=-1		):
							A		,		A      :					Dict      		=					tf.split(lowerCamelCase_     ,				2     ,				axis=lowerCamelCase_		)
							return a * tf.math.sigmoid(lowerCamelCase_		)


if version.parse(tf.version.VERSION) >= version.parse("2.4"):






		def        snake_case__		(    lowerCamelCase_		):
									return tf.keras.activations.gelu(lowerCamelCase_     ,				approximate=lowerCamelCase_		)

		lowercase			:				Tuple   =     tf.keras.activations.gelu

		lowercase			:				Union[str, Any]   =     approximate_gelu_wrap
else:
		lowercase			:				str   =     _gelu
		lowercase			:				Dict   =     _gelu_new


lowercase			:				Union[str, Any]   =     {
    "gelu": gelu,
    "gelu_10": gelu_aa,
    "gelu_fast": gelu_fast,
    "gelu_new": gelu_new,
    "glu": glu,
    "mish": mish,
    "quick_gelu": quick_gelu,
    "relu": tf.keras.activations.relu,
    "sigmoid": tf.keras.activations.sigmoid,
    "silu": tf.keras.activations.swish,
    "swish": tf.keras.activations.swish,
    "tanh": tf.keras.activations.tanh,
}






def        snake_case__		(    lowerCamelCase_		):
							if activation_string in ACTaFN:
														return ACTaFN[activation_string]
							else:
														raise KeyError(F'function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys()		)}'		)






